4.66667

4.7 | 9 ratings Rate this file 256 downloads (last 30 days) File Size: 2.74 KB File ID: #18289

Neural Network training using the Extended Kalman Filter

by Yi Cao

 

10 Jan 2008 (Updated 07 Feb 2008)

BSD License  

A function using the extended Kalman filter to train MLP neural networks

Download Now | Watch this File

File Information
Description

The extended Kalman filter can not only estimate states of nonlinear dynamic systems from noisy measurements but also can be used to estimate parameters of a nonlinear system. A direct application of parameter estimation is to train artificial neural networks. This function and an embeded example shows a way how this can be done.

Acknowledgements

The author wishes to acknowledge the following in the creation of this submission:
Learning the Extended Kalman Filter
This submission has inspired the following:
Neural Network training using the Unscented Kalman Filter

MATLAB release MATLAB 7.5 (R2007b)
Other requirements It requires the ekf function, which can be downloaded from the following link: http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18189&objectType=FILE
Tags for This File  
Everyone's Tags
Tags I've Applied
Add New Tags Please login to tag files.
Comments and Ratings (8)
21 Feb 2008 mahendra shukla too good
21 Feb 2008 lekouch khalid is an intersent work
21 Jul 2008 a s  
28 Jul 2008 piyush singhal it is a good effort pl generate codes for it which can be help ful for mpc
16 Sep 2008 Devanathan M Very nice
08 Oct 2008 x y Great job!
05 Apr 2009 V. Poor  
05 Apr 2009 Vasquez  
Please login to add a comment or rating.
Updates
07 Feb 2008 update description
Tag Activity for this File
Tag Applied By Date/Time
fuzzy logic Yi Cao 22 Oct 2008 09:42:26
neural networks Yi Cao 22 Oct 2008 09:42:26
parameter estimation Yi Cao 22 Oct 2008 09:42:26
extended kalman filter Yi Cao 22 Oct 2008 09:42:26

Public Submission Policy

NOTICE: Any content you submit to MATLAB Central, including personal information, is not subject to the protections which may be afforded information collected under other sections of The MathWorks, Inc. Web site. You are entirely responsible for all content that you upload, post, e-mail, transmit or otherwise make available via MATLAB Central. The MathWorks does not control the content posted by visitors to MATLAB Central and, does not guarantee the accuracy, integrity, or quality of such content. Under no circumstances will The MathWorks be liable in any way for any content not authored by The MathWorks, or any loss or damage of any kind incurred as a result of the use of any content posted, e-mailed, transmitted or otherwise made available via MATLAB Central. Read the complete Disclaimer prior to use.

Contact us at files@mathworks.com